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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationFri, 27 Nov 2009 07:26:23 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/27/t1259332065cu2of88homnym1q.htm/, Retrieved Sun, 28 Apr 2024 20:22:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60814, Retrieved Sun, 28 Apr 2024 20:22:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Explorative Data Analysis] [SHw WS5 ] [2009-11-03 18:13:33] [af2352cd9a951bedd08ebe247d0de1a2]
-    D  [Bivariate Explorative Data Analysis] [WS5] [2009-11-27 14:06:18] [af2352cd9a951bedd08ebe247d0de1a2]
-    D    [Bivariate Explorative Data Analysis] [WS5] [2009-11-27 14:12:45] [af2352cd9a951bedd08ebe247d0de1a2]
-    D        [Bivariate Explorative Data Analysis] [WS5] [2009-11-27 14:26:23] [d9efc2d105d810fc0b0ac636e31105d1] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
-0.907
-1.276
-1.605
-0.657
0.064
-0.365
0.108
-0.032
-0.719
0.291
0.024
-0.221
-0.275
0.082
0.458
0.285
-0.101
0.166
0.802
0.853
0.496
-0.067
0.119
0.344
-0.02
-0.171
-0.863
-0.474
-0.409
-0.711
-0.666
-0.611
-1.387
-1.395
-1.037
-0.933
-0.822
-0.723
-0.798
-0.734
-1.212
-1.311
-0.872
-1.124
-1.172
-0.532
0.371
0.591
0.935
1.103
1.805
1.605
2.691
3.004
3.489
3.163
2.703
1.992
0.512
-0.138
Dataseries Y:
Download CSV
Histogram 
-3.57
1.64
7.25
-17.07
-25.96
-8.35
9.08
20.68
66.51
54.61
34.64
30.69
31.55
24.42
30.58
8.15
9.89
23.86
36.62
40.03
80.16
51.83
27.09
35.84
37.6
30.19
19.47
5.46
16.61
16.79
32.74
33.79
61.63
30.35
3.13
-6.23
-3.22
-15.13
-25.38
-37.14
-54.12
-57.21
-31.72
-26.04
-8.72
-18.32
-53.59
-51.39
-43.35
-58.47
-64.65
-67.65
-96.39
-61.56
-46.21
-55.87
-21.47
-41.48
-56.28
-28.78

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60814&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60814&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60814&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Model: Y[t] = c + b X[t] + e[t]
c-2.02154810947753
b-13.7640240665631

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & -2.02154810947753 \tabularnewline
b & -13.7640240665631 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60814&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]-2.02154810947753[/C][/ROW]
[ROW][C]b[/C][C]-13.7640240665631[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60814&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60814&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Model: Y[t] = c + b X[t] + e[t]
c-2.02154810947753
b-13.7640240665631






Descriptive Statistics about e[t]
# observations60
minimum-73.2330874417867
Q1-25.1833341237623
median-4.08217207281091
mean-2.74123894879648e-15
Q328.542206776087
maximum89.0085040464928

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 60 \tabularnewline
minimum & -73.2330874417867 \tabularnewline
Q1 & -25.1833341237623 \tabularnewline
median & -4.08217207281091 \tabularnewline
mean & -2.74123894879648e-15 \tabularnewline
Q3 & 28.542206776087 \tabularnewline
maximum & 89.0085040464928 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60814&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]60[/C][/ROW]
[ROW][C]minimum[/C][C]-73.2330874417867[/C][/ROW]
[ROW][C]Q1[/C][C]-25.1833341237623[/C][/ROW]
[ROW][C]median[/C][C]-4.08217207281091[/C][/ROW]
[ROW][C]mean[/C][C]-2.74123894879648e-15[/C][/ROW]
[ROW][C]Q3[/C][C]28.542206776087[/C][/ROW]
[ROW][C]maximum[/C][C]89.0085040464928[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60814&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60814&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics about e[t]
# observations60
minimum-73.2330874417867
Q1-25.1833341237623
median-4.08217207281091
mean-2.74123894879648e-15
Q328.542206776087
maximum89.0085040464928


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 1 ;
Parameters (R input):
par1 = 0 ; par2 = 1 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
x <- as.ts(x)
y <- as.ts(y)
mylm <- lm(y~x)
cbind(mylm$resid)
library(lattice)
bitmap(file='pic1.png')
plot(y,type='l',main='Run Sequence Plot of Y[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1a.png')
plot(x,type='l',main='Run Sequence Plot of X[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1b.png')
plot(x,y,main='Scatter Plot',xlab='X[t]',ylab='Y[t]')
grid()
dev.off()
bitmap(file='pic1c.png')
plot(mylm$resid,type='l',main='Run Sequence Plot of e[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(mylm$resid,main='Histogram of e[t]')
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~mylm$resid,col='black',main=paste('Density Plot of e[t]   bw = ',par1),bw=par1)
} else {
densityplot(~mylm$resid,col='black',main='Density Plot of e[t]')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(mylm$resid,main='QQ plot of e[t]')
qqline(mylm$resid)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='pic5.png')
acf(mylm$resid,lag.max=par2,main='Residual Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model: Y[t] = c + b X[t] + e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'c',1,TRUE)
a<-table.element(a,mylm$coeff[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'b',1,TRUE)
a<-table.element(a,mylm$coeff[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics about e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(mylm$resid))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')